Last updated: 2021-02-18
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Knit directory: melanoma_publication_old_data/
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| Rmd | b68b4c3 | SchulzDan | 2020-08-17 | added scores and coxph models. not finished |
| Rmd | 2c11d5c | toobiwankenobi | 2020-08-05 | add new scripts |
Here, we add more metadata and colData annotations to the SCE object which are needed for downstream analyses.
library(SingleCellExperiment)
library(dplyr)sce <- readRDS(file = "data/data_for_analysis/sce_protein.rds")
cur_sce <- data.frame(colData(sce))
cur_meta <- data.frame(metadata(sce))cur_sce <- cur_sce %>%
left_join(., cur_meta[,c("ImageNumber", "Location", "Adjuvant", "IHC_T_score", "Mutation",
"Cancer_Stage", "Status_at_3m", "BlockID", "Description", "TissueType",
"MM_location", "Age_range", "Gender", "PatientID", "relapse")])Joining, by = "ImageNumber"colData(sce)$Location <- cur_sce$Location
colData(sce)$Adjuvant <- cur_sce$Adjuvant
colData(sce)$E_I_D <- cur_sce$IHC_T_score
colData(sce)$Mutation <- cur_sce$Mutation
colData(sce)$Cancer_Stage <- cur_sce$Cancer_Stage
colData(sce)$Status_at_3m <- cur_sce$Status_at_3m
colData(sce)$BlockID <- cur_sce$BlockID
colData(sce)$Description <- cur_sce$Description
colData(sce)$TissueType <- cur_sce$TissueType
colData(sce)$MM_location <- cur_sce$MM_location
colData(sce)$Age <- cur_sce$Age_range
colData(sce)$Gender <- cur_sce$Gender
colData(sce)$PatientID <- cur_sce$PatientID
colData(sce)$relapse <- cur_sce$relapse# create simplified location of biopsies
cur_sce$MM_location_simplified <- NA
cur_sce[grep("CTRL", cur_sce$Location), ]$MM_location_simplified <- "control"
cur_sce[grep("LN", cur_sce$MM_location), ]$MM_location_simplified <- "LN"
cur_sce[grep("skin", cur_sce$MM_location), ]$MM_location_simplified <- "skin"
cur_sce[is.na(cur_sce$MM_location_simplified) == TRUE, ]$MM_location_simplified <- "other"
# add to colData
colData(sce)$MM_location_simplified <- cur_sce$MM_location_simplified
# add to metadata
cur_meta <- left_join(cur_meta, distinct(cur_sce, MM_location_simplified, ImageNumber))Joining, by = "ImageNumber"# unique treatments
unique(cur_meta$Last_sys_treatment_before_surgery) [1] "BRAFi + MEKi" "aPD1" "untreated"
[4] "aPD1 + aCTLA4 or aPD1" "aPD1 + aCTLA4" "chemotherapy"
[7] "aPD1 + aLAG3" NA "aCTLA4"
[10] "MEKi" # group treatment types
cur_meta$treatment_group_before_surgery <- NA
cur_meta[cur_meta$MM_location_simplified == "control",]$treatment_group_before_surgery <- "control"
cur_meta[cur_meta$Last_sys_treatment_before_surgery %in% "untreated",]$treatment_group_before_surgery <- "untreated"
cur_meta[cur_meta$Last_sys_treatment_before_surgery %in% c("aPD1", "aPD1 + aCTLA4", "aCTLA4",
"aPD1 + aCTLA4 or aPD1", "aPD1 + aLAG3"),]$treatment_group_before_surgery <- "ICI"
cur_meta[cur_meta$Last_sys_treatment_before_surgery %in% c("BRAFi + MEKi", "MEKi"),]$treatment_group_before_surgery <- "Targeted"
cur_meta[cur_meta$Last_sys_treatment_before_surgery %in% c("chemotherapy"),]$treatment_group_before_surgery <- "Chemotherapy"
cur_meta[is.na(cur_meta$Last_sys_treatment_before_surgery),]$treatment_group_before_surgery <- "unknown"
# add treatment type to cur_sce
cur_sce <- left_join(cur_sce, cur_meta[,c("ImageNumber", "treatment_group_before_surgery")])Joining, by = "ImageNumber"# add relapse to SCE
colData(sce)$treatment_group_before_surgery <- cur_sce$treatment_group_before_surgery# unique treatments
unique(cur_meta$Treatment_after_surgery) [1] "BRAFi + MEKi" "aCTLA4" "BRAFi + MEKi +/- aPD1"
[4] "aPD1 + aCTLA4" "aPD1" "untreated"
[7] "chemotherapy" NA "MEKi"
[10] "BRAFi" "TVEC" "aPD1 + aLAG3"
[13] "PC" # group treatment types
cur_meta$treatment_group_after_surgery <- NA
cur_meta[cur_meta$MM_location_simplified == "control",]$treatment_group_after_surgery <- "control"
cur_meta[cur_meta$Treatment_after_surgery %in% "untreated",]$treatment_group_after_surgery <- "untreated"
cur_meta[cur_meta$Treatment_after_surgery %in% c("aPD1 + aCTLA4", "aPD1", "aCTLA4", "aPD1 + aLAG3"),]$treatment_group_after_surgery <- "ICI"
cur_meta[cur_meta$Treatment_after_surgery %in% c("BRAFi + MEKi", "BRAFi", "MEKi", "BRAFi + MEKi +/- aPD1"),]$treatment_group_after_surgery <- "Targeted"
cur_meta[cur_meta$Treatment_after_surgery %in% c("chemotherapy"),]$treatment_group_after_surgery <- "Chemotherapy"
cur_meta[cur_meta$Treatment_after_surgery %in% c("TVEC"),]$treatment_group_after_surgery <- "TVEC"
cur_meta[cur_meta$Treatment_after_surgery %in% c("PC"),]$treatment_group_after_surgery <- "Palliative"
cur_meta[is.na(cur_meta$Treatment_after_surgery),]$treatment_group_after_surgery <- "unknown"
# add treatment type to cur_sce
cur_sce <- left_join(cur_sce, cur_meta[,c("ImageNumber", "treatment_group_after_surgery")])Joining, by = "ImageNumber"# add relapse to SCE
colData(sce)$treatment_group_after_surgery <- cur_sce$treatment_group_after_surgerycur_meta$treatment_status_before_surgery <- NA
cur_meta[cur_meta$Location == "CTRL",]$treatment_status_before_surgery <- "control"
cur_meta[cur_meta$Location != "CTRL",]$treatment_status_before_surgery <- ifelse(cur_meta[cur_meta$Location != "CTRL",]$Nr_treatments_before_surgery == 0, "naive", "non-naive")
# add treatment type to cur_sce
cur_sce <- left_join(cur_sce, cur_meta[,c("ImageNumber", "treatment_status_before_surgery")])Joining, by = "ImageNumber"# add to SCE
colData(sce)$treatment_status_before_surgery <- cur_sce$treatment_status_before_surgeryDate_death <- metadata(sce)$Date_death
sce$Date_death <- Date_death[sce$ImageNumber]
#binarize death
sce$Death <- "no"
sce[,grepl("20",sce$Date_death)]$Death <- "yes"metadata(sce) <- as.list(cur_meta)saveRDS(sce,file = "data/data_for_analysis/sce_protein.rds")
sessionInfo()R version 4.0.3 (2020-10-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04 LTS
Matrix products: default
BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.8.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=C
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] parallel stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] dplyr_1.0.2 SingleCellExperiment_1.12.0
[3] SummarizedExperiment_1.20.0 Biobase_2.50.0
[5] GenomicRanges_1.42.0 GenomeInfoDb_1.26.2
[7] IRanges_2.24.1 S4Vectors_0.28.1
[9] BiocGenerics_0.36.0 MatrixGenerics_1.2.0
[11] matrixStats_0.57.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rcpp_1.0.5 XVector_0.30.0 pillar_1.4.7
[4] compiler_4.0.3 later_1.1.0.1 git2r_0.28.0
[7] zlibbioc_1.36.0 bitops_1.0-6 tools_4.0.3
[10] digest_0.6.27 lattice_0.20-41 evaluate_0.14
[13] lifecycle_0.2.0 tibble_3.0.4 pkgconfig_2.0.3
[16] rlang_0.4.10 Matrix_1.3-2 DelayedArray_0.16.0
[19] rstudioapi_0.13 yaml_2.2.1 xfun_0.20
[22] GenomeInfoDbData_1.2.4 stringr_1.4.0 knitr_1.30
[25] generics_0.1.0 fs_1.5.0 vctrs_0.3.6
[28] tidyselect_1.1.0 grid_4.0.3 rprojroot_2.0.2
[31] glue_1.4.2 R6_2.5.0 rmarkdown_2.6
[34] purrr_0.3.4 magrittr_2.0.1 whisker_0.4
[37] promises_1.1.1 ellipsis_0.3.1 htmltools_0.5.0
[40] httpuv_1.5.4 stringi_1.5.3 RCurl_1.98-1.2
[43] crayon_1.3.4